scholarly journals Peripartum cardiomyopathy: An analysis of clinical profiles and outcomes from a tertiary care centre in southern India

2019 ◽  
Vol 13 (4) ◽  
pp. 179-184 ◽  
Author(s):  
Aditya John Binu ◽  
Sudha Jasmine Rajan ◽  
Swati Rathore ◽  
Manisha Beck ◽  
Annie Regi ◽  
...  
Keyword(s):  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Tertiary Care ◽  
Left Ventricular ◽  
Peripartum Cardiomyopathy ◽  
Ventricular Ejection ◽  
Left Ventricular Ejection ◽  
Tertiary Care Centre ◽  
Ventricular Ejection Fraction ◽  
Clinical Profiles

Peripartum cardiomyopathy is a syndrome of maternal heart failure with decreased left ventricular ejection fraction affecting maternal and fetal well-being. We analysed clinical profiles and outcomes in women with peripartum cardiomyopathy enrolled retrospectively from a tertiary care centre in southern India (1 January 2008–31 December 2014). The incidence of peripartum cardiomyopathy was one case per 1541 live births. Fifty-four women with a mean age of 25.5 years and mean gestational age of 35.4 weeks were recruited; 35 were primigravidae. Maternal and fetal deaths occurred in 9.3% and 24.1% of subjects, respectively. Mild-to-moderate maternal anaemia (80–110 g/L) was associated with fetal mortality (p = 0.02). Reduced left ventricular ejection fraction (<30%, p = 0.04) and cardiogenic shock (p = 0.01) were significantly associated with adverse maternal outcomes. Forty per cent of women were followed up after 24.2 ± 17.7 months, and in these women a significant increase in left ventricular ejection fraction was seen (mean 16.4%, p < 0.01); all were asymptomatic. Peripartum cardiomyopathy with poor left ventricular ejection fraction and shock is associated with adverse maternal outcomes, while non-severe maternal anaemia predisposes to adverse fetal outcomes. Significant left ventricular ejection fraction recovery occurred on follow-up.

scholarly journals Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

2021 ◽  
Vol 8 ◽  
Author(s):  
Mohanad Alkhodari ◽  
Herbert F. Jelinek ◽  
Angelos Karlas ◽  
Stergios Soulaidopoulos ◽  
Petros Arsenos ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Left Ventricular ◽  
Ventricular Ejection ◽  
Left Ventricular Ejection ◽  
Clinical Markers ◽  
Ventricular Ejection Fraction ◽  
Clinical Profiles

Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF.Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges.Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF &gt; 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF &lt; 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories.Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p &lt; 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity &lt;5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98.Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention.

scholarly journals Immunosuppression therapy in the management of peripartum cardiomyopathy: A case series and literature review

2016 ◽  
Vol 9 (4) ◽  
pp. 174-176
Author(s):  
Joyee Basu ◽  
Christopher Redman ◽  
Oliver Ormerod
Keyword(s):  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Left Ventricular Systolic Dysfunction ◽  
Case Series ◽  
Left Ventricular ◽  
Peripartum Cardiomyopathy ◽  
Ventricular Ejection ◽  
Left Ventricular Ejection ◽  
High Dose ◽  
Ventricular Ejection Fraction

Peripartum cardiomyopathy is a heart failure syndrome occurring late in pregnancy or during the early post-natal period. The pathophysiology of peripartum cardiomyopathy is not fully understood and various mechanisms have been postulated including an underlying inflammatory process. We here report four cases presenting with acute left ventricular systolic dysfunction. Three out of four of the patients presented with a left ventricular ejection fraction <30% and one with a left ventricular ejection fraction of 35%. All made a full clinical recovery following treatment with high-dose intravenous steroids. This case series adds to the growing body of evidence for the role for immunosuppressants in the management of peripartum cardiomyopathy.

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